Adaptive Neural Control for Safe Human-Robot Interaction
Access Status
Open access
Authors
Date
2017Supervisor
Prof. Ian Howard
Type
Thesis
Award
PhD
Metadata
Show full item recordFaculty
Science and Engineering
School
Mechanical Engineering
Collection
Abstract
This thesis studies safe human-robot interaction utilizing the neural adaptive control design. First, novel tangent and secant barrier Lyapunov functions are constructed to provide stable position and velocity constrained controls, respectively. Then, neural backpropagation and the concept of the inverse differential Riccati equation are utilized to achieve the impedance adaption control for assistive human-robot interaction, and the optimal robot-environment interaction control, respectively. Finally, adaptive neural assist-as-needed control is developed for assistive robotic rehabilitation.
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